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Microbial interactions from a new perspective: reinforcement learning reveals new insights into microbiome evolution.

Authors :
Ghadermazi P
Chan SHJ
Source :
Bioinformatics (Oxford, England) [Bioinformatics] 2024 Jan 02; Vol. 40 (1).
Publication Year :
2024

Abstract

Motivation: Microbes are essential part of all ecosystems, influencing material flow and shaping their surroundings. Metabolic modeling has been a useful tool and provided tremendous insights into microbial community metabolism. However, current methods based on flux balance analysis (FBA) usually fail to predict metabolic and regulatory strategies that lead to long-term survival and stability especially in heterogenous communities.<br />Results: Here, we introduce a novel reinforcement learning algorithm, Self-Playing Microbes in Dynamic FBA, which treats microbial metabolism as a decision-making process, allowing individual microbial agents to evolve by learning and adapting metabolic strategies for enhanced long-term fitness. This algorithm predicts what microbial flux regulation policies will stabilize in the dynamic ecosystem of interest in the presence of other microbes with minimal reliance on predefined strategies. Throughout this article, we present several scenarios wherein our algorithm outperforms existing methods in reproducing outcomes, and we explore the biological significance of these predictions.<br />Availability and Implementation: The source code for this article is available at: https://github.com/chan-csu/SPAM-DFBA.<br /> (© The Author(s) 2024. Published by Oxford University Press.)

Details

Language :
English
ISSN :
1367-4811
Volume :
40
Issue :
1
Database :
MEDLINE
Journal :
Bioinformatics (Oxford, England)
Publication Type :
Academic Journal
Accession number :
38212999
Full Text :
https://doi.org/10.1093/bioinformatics/btae003